CTSN: Predicting cloth deformation for skeleton-based characters with a two-stream skinning network  

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作  者:Yudi Li Min Tang Yun Yang Ruofeng Tong Shuangcai Yang Yao Li Bailin An Qilong Kou 

机构地区:[1]College of Computer Science and Technology,Zhejiang University,Hangzhou 310058,China [2]Aurora Studios,Tencent,Shenzhen 518057,China

出  处:《Computational Visual Media》2024年第3期471-485,共15页计算可视媒体(英文版)

基  金:supported in part by grants from the National Natural Science Foundation of China(61972341,61972342,61732015);the Tencent–Zhejiang University Joint Laboratory.

摘  要:We present a novel learning method using a two-stream network to predict cloth deformation for skeleton-based characters.The characters processed in our approach are not limited to humans,and can be other targets with skeleton-based representations such asfish or pets.We use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse features and wrinkle features forming the overall residual from the template cloth mesh.Our network may be used to predict the deformation for loose or tight-fitting clothing.The memory footprint of our network is low,thereby resulting in reduced computational requirements.In practice,a prediction for a single cloth mesh for a skeleton-based character takes about 7 ms on an nVidia GeForce RTX 3090 GPU.Compared to prior methods,our network can generate finer deformation results with details and wrinkles.

关 键 词:cloth deformation learning network SKINNING 

分 类 号:TP391.41[自动化与计算机技术—计算机应用技术]

 

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